{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "0          6                           148              72   \n",
       "1          1                            85              66   \n",
       "2          8                           183              64   \n",
       "3          1                            89              66   \n",
       "4          0                           137              40   \n",
       "\n",
       "   Triceps_skin_fold_thickness  serum_insulin   BMI  \\\n",
       "0                           35              0  33.6   \n",
       "1                           29              0  26.6   \n",
       "2                            0              0  23.3   \n",
       "3                           23             94  28.1   \n",
       "4                           35            168  43.1   \n",
       "\n",
       "   Diabetes_pedigree_function  Age  Target  \n",
       "0                       0.627   50       1  \n",
       "1                       0.351   31       0  \n",
       "2                       0.672   32       1  \n",
       "3                       0.167   21       0  \n",
       "4                       2.288   33       1  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path='G:/VS_AI/MechineLearn/homework/Logistic/189-133-1-1543819101/'\n",
    "train=pd.read_csv('G:/VS_AI/MechineLearn/homework/Logistic/189-133-1-1543819101/pima-indians-diabetes.csv')\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000002321586AE80>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000023215BE9F98>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000023215C0E550>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000023215C27AC8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000023215C4A080>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000023215C645F8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000023215C72C18>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000023215C92208>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000023215C92240>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "pregnants                       768 non-null int64\n",
      "Plasma_glucose_concentration    768 non-null int64\n",
      "blood_pressure                  768 non-null int64\n",
      "Triceps_skin_fold_thickness     768 non-null int64\n",
      "serum_insulin                   768 non-null int64\n",
      "BMI                             768 non-null float64\n",
      "Diabetes_pedigree_function      768 non-null float64\n",
      "Age                             768 non-null int64\n",
      "Target                          768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "count  768.000000                    768.000000      768.000000   \n",
       "mean     3.845052                    120.894531       69.105469   \n",
       "std      3.369578                     31.972618       19.355807   \n",
       "min      0.000000                      0.000000        0.000000   \n",
       "25%      1.000000                     99.000000       62.000000   \n",
       "50%      3.000000                    117.000000       72.000000   \n",
       "75%      6.000000                    140.250000       80.000000   \n",
       "max     17.000000                    199.000000      122.000000   \n",
       "\n",
       "       Triceps_skin_fold_thickness  serum_insulin         BMI  \\\n",
       "count                   768.000000     768.000000  768.000000   \n",
       "mean                     20.536458      79.799479   31.992578   \n",
       "std                      15.952218     115.244002    7.884160   \n",
       "min                       0.000000       0.000000    0.000000   \n",
       "25%                       0.000000       0.000000   27.300000   \n",
       "50%                      23.000000      30.500000   32.000000   \n",
       "75%                      32.000000     127.250000   36.600000   \n",
       "max                      99.000000     846.000000   67.100000   \n",
       "\n",
       "       Diabetes_pedigree_function         Age      Target  \n",
       "count                  768.000000  768.000000  768.000000  \n",
       "mean                     0.471876   33.240885    0.348958  \n",
       "std                      0.331329   11.760232    0.476951  \n",
       "min                      0.078000   21.000000    0.000000  \n",
       "25%                      0.243750   24.000000    0.000000  \n",
       "50%                      0.372500   29.000000    0.000000  \n",
       "75%                      0.626250   41.000000    1.000000  \n",
       "max                      2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plasma_glucose_concentration      5\n",
      "blood_pressure                   35\n",
      "Triceps_skin_fold_thickness     227\n",
      "serum_insulin                   374\n",
      "BMI                              11\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "NaN_col_names=['Plasma_glucose_concentration','blood_pressure','Triceps_skin_fold_thickness','serum_insulin','BMI']\n",
    "print((train[NaN_col_names]==0).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23219986ba8>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XkhwDXA+8BHg38OKqettC/jHSo3EFIR2630vydUZbQCwHXtDqPwKubMcnA9+squ/UaLuCj857/quAP0lyI3Ad8HTgpAXpXDoEriCkQ5DklcDLgTVV9YMkX2L0HzzAD+qRvWt626gz79y6qvrWPq/98ie8YelxcAUhHZqjgN0tHH4a+LlHmXcrow3mViQJ8Cvzzn0O+N29gyQva4cPAM8eoGfpMTEgpEPzz8Az2yWmCxndP9hPVT3EaJfcfwW+yOg7Nu5vp9/VXuPmJLcC72z1a4GXthvX3qTWxLmbqzSQJM+qqgfbCuJvgJur6uJJ9yWNyxWENJzfajeivwE8A/jbCfcjHRJXEJKkLlcQkqQuA0KS1GVASJK6DAhJUpcBIUnqMiAkSV3/D1k8lXUTrwc1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train['Target'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23219b91128>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#fig=plt.figure()\n",
    "sns.countplot(train['pregnants'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23219c0f7f0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='pregnants',hue='Target',data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23219c2ff98>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Target',y='Plasma_glucose_concentration',data=train,hue='Target')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23219ce2fd0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Target',y='blood_pressure',data=train,hue='Target')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2321a0cb9b0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ni7qZ7Qb8EPjrEML6Aa5faGYLzGxBX+e6IcMa6vziWZf+vDifu4az0XM/A/of5kzmfudkDxdWjeEM5N7GlhHYNSD5eeuNnAc9wnPIG2HYPCn7b/U51zWWwUjKYrDfhjyPvYGz80ca95DXRuh3xOedDxfeMAxbH/L/jdDI/zcY6N6hzvEfpXPfyzQk6mY2Fhf060IIPxrITwjhqhDCnBDCnLaJ2yeRQgixs9DI6hcDvg08HEL4p+aZJIQQol4a6am/AfgAcLKZ3R8/pzfJLiGEEHVQ95LGEMLvAGuiLUIIIRpEO0qFEKJCSNSFEKJCSNSFEKJCSNSFEKJCSNSFEKJCSNSFEKJCSNSFEKJCSNSFEKJCSNSFEKJCSNSFEKJCbDdRH+543PL14cIacdxDHe/ZwJGZA9ky7FHBQx2/OoKwh03XMPQLq4FwaopnmKN9tzn6dZhjmQe9Nkw6BszTRvJwmLIcqT2DHQENDJmHNR35XGM8A9k0lJ9ZXddvE95g/odLU/kI22HzoOv6bY4sHjTt86YMa+dQR/UOlc5t4i0fxTuMe8BjgmtEPXUhhKgQEnUhhKgQEnUhhKgQEnUhhKgQEnUhhKgQEnUhhKgQEnUhhKgQEnUhhKgQEnUhhKgQEnUhhKgQEnUhhKgQEnUhhKgQDYm6mc01s6Vm9riZXdoso4QQQtRH3aJuZm3AN4A/Bo4CzjGzo5plmBBCiJHTSE/9eODxEMKTIYRu4Ebg7c0xSwghRD1YCKG+G83eDcwNIXw4uj8AnBBC+MuSvwuBC6PzCKAju/w8sGed7kbuVVyKS3Eprh0prkkhhL2ogTG1eBoEG+C3bZ4QIYSrgKteuslsQXZtTr3uRu5VXIpLcSmuHSmuEMIsaqSR4Zd24IDMPRNY0UB4QgghGqQRUb8HeIWZHWxm44CzgZ81xywhhBD1UPfwSwih18z+EvgF0AZ8J4TwUA23XtVEdzPDUlyKS3Eprpd7XMNS90SpEEKIlx/aUSqEEBVCoi6EEBVCoi6EEBVCoi6EEBWikc1HNWNm04AQQlhbg98pwFxgf3wz0+7AYuCjuL0GPAksAe4NIfzKzN4HvB54GFgI9IYQ7oln0cwFzgghnDJA+OPwlTv/EUJ41Mz+HDgEOAz4OPAVYDywGtgv3rMS+G12z7HAu/EH5DRgr3jPcmAicCywAbg7+pkfQviPaNsZwLnARTEdHwfeBWyJaabkf26854/xJaQr8P0Cl+Hn77wA3A48ATxVyoMzge8BK2Jc5wN/CmwC7o35Olw+bMT3JuwDdMZ8+Ea8dy7wODA12vVr4DPAidHGfwN+F0LYamavAWYBi4APRL+T8WMmDol59yjwO+APwL7Al/G9EFOBF4E7gKeBOzKbT4h5/iCwBzAJmBHzcRXwY+A3MX/fCXThx120AWui+/XR/ntiud8VbT4OOBw4EvhH4C9j+BtiPuV211I3wOtGBzA/+mlVPewGjo7pugl4roXpyuP6cfy7IYSwKJb7O/D6ksJ/BbAe+Dnw78BJeJ2ZHX+fhu+s/EIsn48BC/C6+xhejzbEePcHHgD+LoTQF9NmwFkxr96L68m9wBVAT8zPT+J1a0JM52djuBfh9ekHwGnx+0K8Lh0J7AZ8Gm9bZ8V8XQ4cGvN0ZczvQ6Ptp8b4uvE6en9M4/F4++2MYUyN984Hzg4hfJCREEJoyQc4ED8PZg0uMk/jlTKdEfNd4NUxMf8Lb9gfjH4XAXfFhHXj4t4LbMUbweaYSQ/HAnoQF8G10d86vMA34QLQh1eMTbEgn4uF0xXDDPFayNzpe/5ZW7qewusu3RNw4Qh4pV4b3d3RvmTL+ui/M0vfC3jFSPfk/lfHtGyMtm+O8XfgFeTp+L03XsvzYGt2T8qT9ux7LfnQFz/rs+/ron1fwRvXhhhGb0xXsjmFtXyAvCrnacg+C2N6c/u2lu5ZUronXd+UlUNuwydK/vPy2lzKj7LNoXS9nG+11I3N8e/z8fvWAcJpVj3sjH/Xx/LPbWx2ujozP+m33vh3zQBpKqdzS+Y/2ZOXW3cpzL74vSN+78n8LqNofymevlKcPy/Flz49pXhTvqwb4Fr3APeX61v59y3xk9vTG8NaFX/vivm5Bd//8zPgz2rS3haK+l34k/G9eKNcEzN/GUXl7I3GPxrdPVkmb4nuLbho58KePqkS/Ty6Uwb34Y0l+c8r0ha8UaQKswVvXMnPM9n3riyMVJk30F9kumMYyf0CLqYhpvdu4K+z9G7M0vJkycZVuMgme1I6QuneECtYCrMD+H/xb6qUyeY8/JT+ZFtue6q0w+VDR/z70yzcvmh3XkbpwdKZlUVfFt6WLPzeLH098e+mzP/98e+G+PcXbCu8+YOnh/4inXpyfWwrXM+X/D5D8SBK15PflSWbu+lfN5LdtdSNbrwepnLM42p2PezC69Zzo5CurnjP9VmZJ/FN8aXwt+APm0UlG+ZnZZm35XT/X2Tf04OgN8bbU/K7hqKObsTracqHVKeSiCbBTvU3PUADRdtL1zeyrfCndpceLk/Tv65dkqUr5UNXdHdk8T4OnII/dHuAN8fPslq0t5Vj6nuGEH6Av9qsxxviv+CvHylBbTFz/iLeMwZ/pUtPsbbo3ideb6N/j3dC/H1udLfFa4a/DrZRiNwWvADHxXi2As/Gz+9iOCGGmQrhafwhBP7qNQavfM/H+4j2pcIFF4RXxe+7A3OAf4ruJHQpLZ0U8xpbo33jgekxvEeBsZn/1HDyytWGD128AW+4yXaLacnzoDuLf3cKIe4BPpSlYaB8eDpe2yP+/ZMYXrL9sdL9bcCuFEMbz8e8I6YpF/UVMaxJ0f8KvOGl8FM5T4z+06vzM3jdSjYQ82VTZm83XuZjKd5O0rlFIcaTNs3tir/CT4l+n8vCMfqLxVI8v5Ndud3t8feh6saYmK5JWVpaVQ/H4eU2fRTSlYY0T6N/p6MtXt+ShT8upj/5SeG9juIB04MPSXZm9qY20U0hxLvEfPlE/C35JYa1C/4AfZHiIQne3lLal+AibhT1Kb0VJdFOYe9K8eBNtiyN31NaH6V/L/1Wis5qCq8txnd0vNYGHAxcg5dXG66bV1Do4NC0sKd+I/BN/KnzCuBqXLTSUzVVirwn3o0XXkp8J8VTL4lYJ0WvIX8NSyLxdeCR6C/vWXTgY88pjNQLeAEf8sl7fcmu/PUvPYi2xPC7s7DKr1d5+tKQUBf+AHua4pVwbXbfjygENsRw58f4kv9yDyrvcQSKCrsFb+x3Z+lKbyc9Mc356+hGfOda3vsaLB+SHWn8NOX9qqxcUppz/7dTDBeVX92fpxgaSHXgyfj9hcz2dM9zsTxfyO4p9/rzHtJqisb0eOla3jvbijfUToqho/uy63l6ejKb+wawe6i6kcJaTfGa3ap6mMJbFvN/YyyrVqZrCy5o+ZBIHlc5/MC26c7fvJ7FBfmqeG1zyV96q9uE17PvlsIYqF32ZOFvHSDc9EntKfl5nv5vSPmQT192LQ2bpjDzUYRU9tdTDEv9mv5vT5uA/4z3HYSP8a+oRXtbOVH6QeB84Bzglpj4XWOivgV8BH9Sr6V4Cm/AJ2AOwScVVuMF+Wn8FXwPikmz7+OTjJ+JGbEr8OUQwifMbBn+9OvDn959eK/jshjPWuBX8b4TgNeHENaamQFfA96CT3D04BODY+L32XgBfx/4VQjhOQAzG4sPf4zHJ1UOiHG+OqbhBPwBdyfwX3jj3YT3sMEL9p0xjjF4A/8yPunzKXx+YhPeqwEXniNifqRX0B7g2piuS+I9e2Z5sAvekzgCn4hZE/PsEXxeYhFwdAjh9UPkQx8+KfRQDOt9Me4DgL0pejiTYt6ui3m9Cp/cmob3ut4e8+EafJLtNrwuHIH3wjpjWu4KITxoZh/BJ89mxHx4hKJXeXMsu7H4uGMbxYNzH7y3Y/jY/A/xRrIr8LcUk1ZH4sKxMtqwIZbbvbhAbIplexXeo7otlsOBeONdH7/3sxuGrBsPAW/E3wqeBX6fpaXZ9bADr4PT8Qf/ncB/tChdHcDpMV/vivnYE8tqJt6+N1L0lKfH9P8YnxTfLebJe6L/ffHJ7c+FEF4ws+txbXgP3unpxt8gpuBvWbfiHcov4nX+UrxtLYvu3vj9n/EH1Z/Gcjg62nFttGUjRX36Xfw7A2+X++AT6hOBy+O1T8X4x+Ptc3YM7xLgq9H/UrxDMgfv6M7C28en4z2vAy7G6+mFMY1PhBCeiXl+BzXQ8mMCzOwYYFMI4XEz+za+CuIoPJH/Hc+8ffHM2II37vTUPwFvnD8MIbzJzGYCB4YQ/isLfzY+9LBLCOFbA8R/Nj7m/lbglcDSEMJNrUrvQES7e0MIqwa49la84h+Oi/CjeOFv49/MDsef2qvxBj4Nrxz/SWwog8SR8uAQvNHMxBvg4hDCI42mJdq1Eh+SWZzbNdLwhdgZiLr1Supog8OG3WpRHzBSszNDCLeUfpuMC+9heA/pyRDCU9n1lw62CSFcGN0zQghnlsK5pfxb+j3eu821PPws7HJczXT3s3Eg23I/tfgv3zdQPgx2XymuevJhxmC2DmDXvBDCPDObl4U1lHvGUHE3UB4raoi7n7uZdg/nHqV6OJrp2h75tmKEacvdM0YY13DuFUOFneuCmV2V3PHagO15MEZlnfoAvA64JU9QCGGDmZ0X3V8e4J7/M4B7RS5i8fcLoBCwGN6Z+e8l/+l6Hv5AcTXTfUHJjgvShUHSUYv/l9KZ+ykJ+WB5kPtNQ1QjSdeK3NZh7Er33FsKazD3zTXYUk95zKgh7oHczbR7OHer6+Fopmt75Fsq45GUb3LfnLmbUUa5LYOFfUGpDV5QvlaLsLe0p25mR+Ljp2kj0QrgZyGEh+P11wKEEO7NBSGEsLLWp5SZ7Rf975fuTb8nP+m3gfyXr9eYrr1DCKvNbO8sjGHdg9ldti/Lg+nAuMzebmCfEMKSsn9gj/R7HmY5X4bLs5HmxUBpKtnVtPCFqDpDtZFa20/LRN3MPoFPkt5IsRRqJr4T8sYQwpdK/vfDJ2Tm4cvrptL/X+alWfVl+ETGl4AbQgh/nIWxOz6h2olPQFwZQrg+XvsmvkzM8ImKB/CJzCPxCZSN+Hj+rjGu3N2Lr/aYgr/djMGXYeZp+Bt8QiRxJT4ZHGKcj+MTI3fiO9Y+i0/g9OITQavx8e5X4w+/E+O9m4DPxXxJ/6NwCz6R8gn8yX8qxQTg5hjOjcA3QghPxPQvxFfY3IBPsH4LL48l+JvTbjHstDzMBnGH0vdufGLxCXxScX98MvQQiuWURrF0azDSyoAuvIy/ia+Y+kZ8Fb0aL6uD8Po0PaZ1Ob6cLt/QldybY14dii+53I9i5+AzMc17xHiXx7j3oZgkOxMIIYTDzezRlwwt3ONi+t8d/U/BJ9VmRPum4BOHaWdkchu+pDTl6xiKyeV8YjutokjutDIiLZlrx+vVbvH6N4G3xfiT+w94z+8V+GTsaRT/sawTrzNjKepacqfVIrtGG9MKj1RW5bY5UNnWU97fxOv0p/DyS2X8WrxdpDLeL37fh2J/xQa8jRxCsbZ9j+hvIT6ZPwkvmwModm1347r0a4qVWxvxMtuNYhXe7jGusRS7aNMKmrFZPqVJ4JRfaSe80T8P0yTvFrz9vyb+PgEvv/V4+X4rhPC9IfKylLOtW9KY1ljPwVc+rMJF5U62XTbUg4vCg/G+J4C/w8XuanxytR0v/HvxpUCL8BUcvwf+lWKZXx++MSatRd2EC1da274xXk+TsfkSrrSSpLzzrFmfPnxCsTfG04UvwUybanrw7e9pCVVaU5wvl/riEOGn5VNpY0TaSLI+fq6O7rTEM6337sVX3OS7B9OmjOTuyMJM+TpUPuVL0tLS0ecodrWmT1f8/Xm88S6MedCOb+1+Dt+CnW9gypfUlXeAlt2bMne+8SW3r9nlnMprC8XmooHcT2V2bMJXi+Tlvj5zr6f/8rlyXHk55O6tMU/upmgDaUNYuj+v84FiOXB6qGzK8i3Vl7SctezuzeLojHaPpLw7KTbd5GWaL0/Ml/JuzfJpK8Vmnkbab6oTqYySO63iSX7WUyxJTGUYSu6kOVspdoumZdk9md/fZvF3RPfD+MP4WuCLNWtqPQ9KAAAMk0lEQVRvC0X9EbxXdXfMiC9RCEMunqmhlTM2VYatMYxUIdJ69LxhpgqX7l0dMzJfG5rH8ePs3k7673K9j/4N/+KsgLsoGuJq+q/r/UrJvSTamvynzTc3Zja103/X2WqKNeRb4/dUafpiPuYV+En6N8A8jZuAn5TyJcWbhC/5743pHiofHsjc3fgb0ebst2UUopHWgyeBeBIXs7SLdSn+8E4PmHL5DPfJxa0bX6aXHtKDuZP/B0r330//rd//HvM9HSGxEV96mWzN3X14j/8+ijqd3hRTHuaCVHaPzb534efNJPfSUtnel7nTgzoX4lSOfYO4c6F7fJiyXpS5N+N16b7seifFstKB3Juje0v8NFreye5Upk/Tf2186qQFiq386b5/oX/7fYyik5HseKpUZvcPUUb5tcHKKG9XfXj934p3QFPb68Xf8FL7zDdlpn00AV8uuj/wyMtB1NMBT+txQbsqM/QZ/LyXVME3Ugh+6tFsxJ/YqUdR3hgw1FM4FWISnfzJuxVfP5qevJ3A5yl2iz1Asba2J6YlbY7pwNfBpt7N+qwQHyu501M47ZRL4pJeFbfiop4qYA/FWSWpMjydFXTqkaRefohxpp78FnyYJm/s6W3guRhvuZeazoRJ9uVng6yjv0Cso2g43fhDKwlfGqJJ+ZuX51a8QafNSpvxh9tKit7bWrxhJRtWR3d62zowK59UJvmuvHwjSe5OPciUx8m2lJZevJG+ED/rcGFNr8OpjjwabewqudP19RTC+XyWTy/SX2CSO+XLTRRvEr0xT1L5b6DoxKR0PE8hTu34m206BqE92tU1gHsTxYackPlJ7lWZzWV3EpnUZlIPdNkQ7uQ/nV0ykvLuwR8Iz2ZluYZCwPsoznVJZZzHnToWffjSWyiEfjXFgzN/O0lnL6Uyyo9r+N9Z2aaHaSqzNfQ/MiFpXap/+cbDZHfSozTUV+7YduEP1bdTnG+1Ktp4YS3a27JjAkIIt+Frr5/BC3IaxdMLijHggA/JTKL/jsl0wt746D/57cE32PwRnkm9eCVoxyvqnRQ7Dcfj41jLKcS0i2Jr/i4xvH+Pdj6BvwLfn9xm9mq8IvwmxrMvhbBOwMfLUiHn7hdj3FNjXL14JRgX00jMl2TLarwwx0U7wTcnBYrx6DR2mnZTHkqxbX8MvhkiHX1wHz5m3x2v3RrtexKffX8k+k9vA2MpNocFijHf3N2WxfXKrEw68Deavnh9PD5+TLT7MLwsx8Zr+1LsTRgf82hWDDdtaV+GDzX9Orrvj7Y/aWZzo/vx+Nkliyt374KXXT6W+UBM99po9xP4w/NJXCTPwR+Cu0f/nRRHMVjJnRp+qqNpC3tqxGksta/kTvMQJ1CMn7bFPEnb7HeL+ZvcY/Gx4F3i73vjuycX4mX9ZAjhcPytNnefgZfx+2K6tsT70xxKwMWlbRD32GjjzCwPx1OMy5fdKZ1p7mIcIyvvpXh7vZmizBfhG51SHv8ws2UXXHgT92T+OszsVXjn54GY51A8AFK92DVLezreIpXZ2RSCntprKrM9Y/6Mi2FNxssoHUMyNQtvl5gnaX4kbWpaibfFe2I84/C5o08Bt4YQXgkcgw/BnEgNjNbmoyvxsaFxeMN5PcUkyhbgz3Cj1+FPu5uBP8efem/DG/b38cayO77b7iTgv+FisQbfobcIz+g/wTPyBLyg/i9eQdqAD+OV4kMUhXsZXpE7cEF+TYx7IHc3frjOkzE9m2PaBnK/BRf3I+LvK2IaZuBDU09Ev+n1di2+G68XL/gpMR2deK9rMv4qNpFCLLoozlK5E6/AV+O9wI/HvO0NIZxtZh/Gjy59A94I/wh/mBwcbRgX8yGNrx8X05272/D9BPtSNJJ9KSbyVuIPyn3jfb/BH5CnUJwoeVAMb3H0l9wP4g//ZM9/4Q3hSLyhP4JPJE8qXeusw31XzMuB3MvxTsKrYt4uwEXoELx+DuY+EK/DI7FlOS5kr4rpGsqugdz1xJXn4QF4/ewcxH0g3jnrxOvXAVnYA7nbKCYq5+PzIakX+mmKYcrk/iKxpxtC+KCZfZ/ISNzD+cXb+7W4IJ+M7yBNZ/6E6F6Mb9KrNS6L7g+Y2b9mfpM7PSDfT7EzOR2p8RZ8KDZ/uBwZ7bwar3dL47XFIYTbqZFR3XwUe73X4JVkMd4Y3o037JSwvNeVerRb6S8av8W30KazRK6L/g/Fd6l24QKVrt2FD01MxDOrjeLArz6KXonFe9JqgGa4H8B30KZjAbrwipVm5I+O/ifhD4DleENKjI9pSa/QhvcmJ+M9zFfgvdPpMa3vi+FNpnjVuy6Gn/IspbeNYjVPOqKglnwIuGCPi2l5EV/NcxbF6XIHUfRsxmfll8JnhO5G7lVcoxOXqI20omkVrn3dpetp7mEy3rmYShySCSGsGzb0Vo2p1zjuvox4nCTeo0yJeQh//UjjoeUjZNMYV/qUx9e/Q/8xs/KE0nspxoO34r2FfExuVZPcadIwH9N9hmIyNP2Wj+2lccz0dwv9J0/z9Kfx3K2lcNKQyBOl3/ronydprDbZvbKGdCXbOkvhDWRX+u3hkr9HRug+OPveVQp7pGEprtbHtYH+49SBYp6gGe40Zt2duZsVdivjSqvS0oqhRyjG7/8pC38Jvspvbbx+Kj509qPtOqaeMLNFZrY5+2w1s2BmAX91O8DMNgPfxnuCadzxz+PfLXgPO4l3ErbNFGs/02qXxPtjZiTBMorlRgD/QHFMb8B7vluz63s0yW34kEt6I0juqRRLm3oo3k7WxXSkdcJt8XruPz3VU+9offzNKJbEJRu+leVBnmdQrONOcXXjQx/DpSutn14dw0qHJKVjY1OPMP1TBvDX/zS5m+yo2R38uIh0bSv+sKorLMXV0rjSm3Oq86ljlTo1i+m/OqVed8Dr6z9THIXbrLBbFVcabUiT62mVUH6cdIj5lh6GuwLPhhB+FfP0EGpgNP5H6T64GP0J/lqehDnfGZU2OBD/vpLiVLpkYxo22QUfQ59EISDTKNYxpzDSJEZ6sib/aSKnLX668DeDFPb6UlyNuudTbL4BHyJKYthBIXxEm9OKoDZceHfN/G/BH04pjb34hNmYeO1gislNA/5nlgd5nvXhD4rUYxhD8d+Uak3XYooyG4MPpfVmZbJX/H1zTMN4ijXqAR/731yL28z2LV07vdZ7FdeoxvUoXi/eEcs6tbO0ImVfigURjboDxUKAZofdirjSMGQ6R38sPvSaNkQ+StFJPQofTu0GJprZbvHe9FAZktE4++UWXNg34GNEN+OTnd/Ex3p3wY8H+C4+xgy+6/GTeGaei29auhD4KzwT/hp/wn0bF/S1uCAGfCL076M7iekivNf6fnyS805812MaW/8wPmk4FR8DnxmvNer+HP5/H4/CN0i9CPwy2jMNnwg5Oab7enyHaLL7Ynwi+HR8+CWtHpqKV7DL8d7yyVk634a/hbwZn3Rchk/gteH/CzTl2dRox+lZnr0jut85TLrmxbTciE8qpUp5WCzLB/FxwLmxLHfBy/2H+HzH6/BhnEPxiv1iDe5efLweimWBZ9V4r+IavbhS2Gvw3bgn4vVzHf5mcCd+JDdNdrcy7FbEdT4+H/Vi/O09eMfyYLxdLcUXZzyDa8eDeFu/kBoY7YnSbwPfDSH8LnPvg29Muhb4UHbteoAQwvvM7Mf4tvwxeA9wZQhhlfkxsKdSCM4LIYQ74+9ppUYSpHTPG6KfNyS7Qgh3tii9Myl6wi8dV5vZfRveGJ7J/CW7f5/sjddfSmcI4eYs7ENjnkwFbivd00vxsLoty7PjYhhvyNOf8qaWNGVhpaMLVpbSkhr5iMIXYmfFzCbiZzs9ZX5q7SG4JrSHeGZ+TeGMpqgLIYRoLaMxpi6EEGKUkKgLIUSFkKiLnQIz6zOz+83sATNbaGavj7/Pikts/y7zu6eZ9ZjZFdE9z8wu3l62CzESJOpiZ2FzCOHYEMIx+Mqqv8+uPYmv1kj8KcUWciF2KCTqYmdkd3wZbGIz8LCZzYnu9+KHvAmxw7G9/kepEKPNBDO7H9+XsB++fjrnRuBsM0v/yGAF/f+PqRA7BBJ1sbOwOYRwLICZnQh838yOzq7fhv+3refwf0wgxA6Jhl/ETkcI4S585+5e2W/d+CFKH8d3vwqxQ6KeutjpMLMj8V27HfjRCYl/BH4bQugw02myYsdEoi52FtKYOvhZNR8KIfTl4h1CeAitehE7ODomQAghKoTG1IUQokJI1IUQokJI1IUQokJI1IUQokJI1IUQokJI1IUQokJI1IUQokL8fxmePioxFY1zAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "BMIDF=train.groupby(['BMI','Target'])['BMI'].count().unstack('Target').fillna(0)\n",
    "BMIDF[[0,1]].plot(kind='bar',stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2321a3c7748>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "BMIDF=train.groupby(['Plasma_glucose_concentration','Target'])['Plasma_glucose_concentration'].count().unstack('Target').fillna(0)\n",
    "BMIDF[[0,1]].plot(kind='bar',stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2321c54bcc0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_corr=train.corr().abs()\n",
    "plt.subplots(figsize=(13,9))\n",
    "sns.heatmap(data_corr,annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "pregnants                       768 non-null int64\n",
      "Plasma_glucose_concentration    768 non-null int64\n",
      "blood_pressure                  768 non-null int64\n",
      "Triceps_skin_fold_thickness     768 non-null int64\n",
      "serum_insulin                   768 non-null int64\n",
      "BMI                             768 non-null float64\n",
      "Diabetes_pedigree_function      768 non-null float64\n",
      "Age                             768 non-null int64\n",
      "Target                          768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plasma_glucose_concentration      5\n",
      "blood_pressure                   35\n",
      "Triceps_skin_fold_thickness     227\n",
      "serum_insulin                   374\n",
      "BMI                              11\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print((train[NaN_col_names]==0).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pregnants                         0\n",
      "Plasma_glucose_concentration      5\n",
      "blood_pressure                   35\n",
      "Triceps_skin_fold_thickness     227\n",
      "serum_insulin                   374\n",
      "BMI                              11\n",
      "Diabetes_pedigree_function        0\n",
      "Age                               0\n",
      "Target                            0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "train[NaN_col_names]=train[NaN_col_names].replace(0,np.NaN)\n",
    "print(train.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 直接中值处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pregnants                         3.0000\n",
      "Plasma_glucose_concentration    117.0000\n",
      "blood_pressure                   72.0000\n",
      "Triceps_skin_fold_thickness      29.0000\n",
      "serum_insulin                   125.0000\n",
      "BMI                              32.3000\n",
      "Diabetes_pedigree_function        0.3725\n",
      "Age                              29.0000\n",
      "Target                            0.0000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "medians=train.median()\n",
    "print(medians)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pregnants                       0\n",
      "Plasma_glucose_concentration    0\n",
      "blood_pressure                  0\n",
      "Triceps_skin_fold_thickness     0\n",
      "serum_insulin                   0\n",
      "BMI                             0\n",
      "Diabetes_pedigree_function      0\n",
      "Age                             0\n",
      "Target                          0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "train= train.fillna(medians)\n",
    "print(train.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['pregnants', 'Plasma_glucose_concentration', 'blood_pressure',\n",
      "       'Triceps_skin_fold_thickness', 'serum_insulin', 'BMI',\n",
      "       'Diabetes_pedigree_function', 'Age'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "X_train=train.drop(['Target'],axis=1)\n",
    "X_feat_name=X_train.columns\n",
    "print(X_feat_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>0.639947</td>\n",
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       "      <td>0.166619</td>\n",
       "      <td>0.468492</td>\n",
       "      <td>1.425995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.844885</td>\n",
       "      <td>-1.205066</td>\n",
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       "      <td>-0.012301</td>\n",
       "      <td>-0.181541</td>\n",
       "      <td>-0.852200</td>\n",
       "      <td>-0.365061</td>\n",
       "      <td>-0.190672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.233880</td>\n",
       "      <td>2.016662</td>\n",
       "      <td>-0.693761</td>\n",
       "      <td>-0.012301</td>\n",
       "      <td>-0.181541</td>\n",
       "      <td>-1.332500</td>\n",
       "      <td>0.604397</td>\n",
       "      <td>-0.105584</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.844885</td>\n",
       "      <td>-1.073567</td>\n",
       "      <td>-0.528319</td>\n",
       "      <td>-0.695245</td>\n",
       "      <td>-0.540642</td>\n",
       "      <td>-0.633881</td>\n",
       "      <td>-0.920763</td>\n",
       "      <td>-1.041549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.141852</td>\n",
       "      <td>0.504422</td>\n",
       "      <td>-2.679076</td>\n",
       "      <td>0.670643</td>\n",
       "      <td>0.316566</td>\n",
       "      <td>1.549303</td>\n",
       "      <td>5.484909</td>\n",
       "      <td>-0.020496</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "0   0.639947                      0.866045       -0.031990   \n",
       "1  -0.844885                     -1.205066       -0.528319   \n",
       "2   1.233880                      2.016662       -0.693761   \n",
       "3  -0.844885                     -1.073567       -0.528319   \n",
       "4  -1.141852                      0.504422       -2.679076   \n",
       "\n",
       "   Triceps_skin_fold_thickness  serum_insulin       BMI  \\\n",
       "0                     0.670643      -0.181541  0.166619   \n",
       "1                    -0.012301      -0.181541 -0.852200   \n",
       "2                    -0.012301      -0.181541 -1.332500   \n",
       "3                    -0.695245      -0.540642 -0.633881   \n",
       "4                     0.670643       0.316566  1.549303   \n",
       "\n",
       "   Diabetes_pedigree_function       Age  \n",
       "0                    0.468492  1.425995  \n",
       "1                   -0.365061 -0.190672  \n",
       "2                    0.604397 -0.105584  \n",
       "3                   -0.920763 -1.041549  \n",
       "4                    5.484909 -0.020496  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 数据标准化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "# 初始化特征的标准化器\n",
    "ss_X=StandardScaler()\n",
    "# 处理数据\n",
    "X_train=ss_X.fit_transform(X_train)\n",
    "X_train=pd.DataFrame(columns=X_feat_name,data=X_train)\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.639947</td>\n",
       "      <td>0.866045</td>\n",
       "      <td>-0.031990</td>\n",
       "      <td>0.670643</td>\n",
       "      <td>-0.181541</td>\n",
       "      <td>0.166619</td>\n",
       "      <td>0.468492</td>\n",
       "      <td>1.425995</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.844885</td>\n",
       "      <td>-1.205066</td>\n",
       "      <td>-0.528319</td>\n",
       "      <td>-0.012301</td>\n",
       "      <td>-0.181541</td>\n",
       "      <td>-0.852200</td>\n",
       "      <td>-0.365061</td>\n",
       "      <td>-0.190672</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.233880</td>\n",
       "      <td>2.016662</td>\n",
       "      <td>-0.693761</td>\n",
       "      <td>-0.012301</td>\n",
       "      <td>-0.181541</td>\n",
       "      <td>-1.332500</td>\n",
       "      <td>0.604397</td>\n",
       "      <td>-0.105584</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.844885</td>\n",
       "      <td>-1.073567</td>\n",
       "      <td>-0.528319</td>\n",
       "      <td>-0.695245</td>\n",
       "      <td>-0.540642</td>\n",
       "      <td>-0.633881</td>\n",
       "      <td>-0.920763</td>\n",
       "      <td>-1.041549</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.141852</td>\n",
       "      <td>0.504422</td>\n",
       "      <td>-2.679076</td>\n",
       "      <td>0.670643</td>\n",
       "      <td>0.316566</td>\n",
       "      <td>1.549303</td>\n",
       "      <td>5.484909</td>\n",
       "      <td>-0.020496</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "0   0.639947                      0.866045       -0.031990   \n",
       "1  -0.844885                     -1.205066       -0.528319   \n",
       "2   1.233880                      2.016662       -0.693761   \n",
       "3  -0.844885                     -1.073567       -0.528319   \n",
       "4  -1.141852                      0.504422       -2.679076   \n",
       "\n",
       "   Triceps_skin_fold_thickness  serum_insulin       BMI  \\\n",
       "0                     0.670643      -0.181541  0.166619   \n",
       "1                    -0.012301      -0.181541 -0.852200   \n",
       "2                    -0.012301      -0.181541 -1.332500   \n",
       "3                    -0.695245      -0.540642 -0.633881   \n",
       "4                     0.670643       0.316566  1.549303   \n",
       "\n",
       "   Diabetes_pedigree_function       Age  Target  \n",
       "0                    0.468492  1.425995       1  \n",
       "1                   -0.365061 -0.190672       0  \n",
       "2                    0.604397 -0.105584       1  \n",
       "3                   -0.920763 -1.041549       0  \n",
       "4                    5.484909 -0.020496       1  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FE_train=pd.concat([X_train,train['Target']],axis=1)\n",
    "#X_train.to_csv(path+'FE_pima-indians-diabetes.csv',index=False)\n",
    "FE_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "FE_train.to_csv(path+'FE_pima-indians-diabetes.csv',index=False)"
   ]
  }
 ],
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